| def normalize_image(img_npy): | |
| """ | |
| :param img_npy: b, c, h, w | |
| """ | |
| for b in range(img_npy.shape[0]): | |
| for c in range(img_npy.shape[1]): | |
| img_npy[b, c] = (img_npy[b, c] - img_npy[b, c].mean()) / img_npy[b, c].std() | |
| return img_npy | |
| def normalize_image_to_0_1(img): | |
| return (img-img.min())/(img.max()-img.min()) | |
| def normalize_image_to_m1_1(img): | |
| return -1 + 2 * (img-img.min())/(img.max()-img.min()) | |